code: def safe_save_model_for_hf_trainer(
      trainer: Trainer, tokenizer: AutoTokenizer, output_dir: str
  ):
      """Helper method to save model for HF Trainer."""
      # see: https://github.com/tatsu-lab/stanford_alpaca/issues/65
      from torch.distributed.fsdp import (
          FullyShardedDataParallel as FSDP,
          FullStateDictConfig,
          StateDictType,
      )
  
      model = trainer.model
      save_policy = FullStateDictConfig(
          offload_to_cpu=True, rank0_only=True
      )
      with FSDP.state_dict_type(
          model, StateDictType.FULL_STATE_DICT, save_policy
      ):
          cpu_state_dict = model.state_dict()
      if trainer.args.should_save:
          trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa
          tokenizer.save_pretrained(output_dir)
  
file_path: hf-notebooks/sagemaker/25_pytorch_fsdp_model_parallelism/scripts/run_clm.py
imports: import os
  import argparse
  from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, default_data_collator
  from datasets import load_from_disk
  import torch
  from transformers import Trainer, TrainingArguments
  import torch.distributed
label_desc: Use image node_id 1 for calling a global function with example usage: safe_save_model_for_hf_trainer(trainer, tokenizer, output_dir) without return types